![]() ![]() Therefore, it is inevitable to study music emotion automatic recognition technology and realize automatic emotion labeling of music works. Emotional annotation of massive music works based on artificial methods is not only a huge workload, but also the quality cannot be guaranteed. To achieve emotion-based music classification and retrieval, it is necessary to label music works with emotions. Therefore, emotion-based music retrieval is an important part of meeting people’s personalized music retrieval needs, and it is also an important development direction of current music retrieval. There are relatively few intelligent classification of music emotions and emotion-based search services. In order to obtain a better user experience, in recent years, various listening platforms have begun to provide song recommendation services with different moods. Recommend similar songs in the music library according to the needs of users and the songs that users often listen to. For these needs, many music websites have also launched music recommendation services. The traditional methods of retrieving music information can no longer meet people’s needs for intelligent and personalized music retrieval ( Casey et al., 2008). This method is currently the most commonly used method. Traditional music retrieval methods are based on classification tags, such as retrieving songs by song name, artist name, and album name. With the rapid increase of music file data, how to use computers to complete fast and effective music information retrieval has become a basic demand of people in the current society. Music has been integrated into all aspects of life including waking up, eating, shopping, learning, sports, driving, education, medical care and so on. During the medical process, appropriate music will be selected to provide psychological counseling to the patient. Exciting and rhythmic music is played during the party, which can set off the joyful atmosphere of the party. Playing soft music during a break can soothe the nerves. Playing rhythmic music during exercise can increase the enthusiasm of exercise and reduce exercise fatigue. Stores often play dynamic music to arouse customers’ desire to buy. The changes in creation, storage, dissemination, and technology have made music play an increasingly important role in human social life. With the development of science and technology, the creation, storage, and dissemination of music have all undergone tremendous changes. Music plays an important role in the history of mankind, and music is integrated into all aspects of human life. Music appeared earlier than language, and human beings are born with music to express feelings. Through experiments on public music data sets, the experimental results show that compared with other comparative models, the MER method used in this paper has better recognition effect and faster recognition speed. BP neural network with ABC algorithm can improve the global search ability of the BP network, while reducing the probability of the BP network falling into the local optimal solution, and the convergence speed is faster. The ABC algorithm is responsible for adjusting the weights and thresholds, and feeds back the optimal weights and thresholds to the BP neural network system. The output value of the ABC algorithm is used as the weight and threshold of the BP neural network. This paper introduces artificial bee colony (ABC) algorithm to improve the structure of BP neural network. Because the traditional BP network tends to fall into local solutions, the selection of initial weights and thresholds directly affects the training effect. In this paper, an improved back propagation (BP) algorithm neural network is used to analyze music data. Second, it is difficult to analyze music emotions based on the pitch, length, and intensity of the notes, which can hardly reflect the soul and connotation of music. First, the emotional color conveyed by the first music is constantly changing with the playback of the music, and it is difficult to accurately express the ups and downs of music emotion based on the analysis of the entire music. The existing music emotion recognition (MER) methods have the following two challenges. Paying more attention to the emotional expression of creators and the psychological characteristics of music are also indispensable personalized needs of users. ![]() At the same time, the demand for music organization, classification, and retrieval is also increasing. Music plays an extremely important role in people’s production and life. Zhejiang Conservatory of Music, Hangzhou, China.
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